Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Differences in the use of artificial intelligence between czech and polish students: a comparative study of approaches, attitudes, and experiences
0
Zitationen
2
Autoren
2026
Jahr
Abstract
Introduction: The increasing presence of artificial intelligence (AI) in education and daily life has created new opportunities and challenges for students. Despite growing interest, little is known about how middle and high school students in different countries use AI academically and personally. This study addresses this gap by examining the extent, purposes, and perceptions of AI use among Czech and Polish students, highlighting cross-national differences and the need for educational strategies that support ethical and effective AI engagement.Research Aim: The aim of this quantitative study was to compare how students in grades 5–8 in Poland, grades 6–9 in the Czech Republic, and high school students (aged 11 to 19) use AI in both academic and personal contexts.Method: The research was conducted through a questionnaire survey in May and June 2025, with 335 respondents (171 from the Czech Republic and 164 from Poland).Results: Czech students used AI more frequently and intensively for educational purposes than their Polish peers. Significant differences were found in usage frequency, purposes, sharing AI-generated outputs with classmates, feelings of guilt related to AI use in studying, and perceptions of AI as a supportive educational tool. Polish students tended to be more cautious, using AI only when necessary. Chi-square tests confirmed several statistically significant differences between the groups.Conclusions: The findings highlight the importance of systematically integrating AI into school curricula and emphasizing ethical AI use, critical thinking, and awareness of artificial intelligence in both countries.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.644 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.550 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.061 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.850 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.